15:56
1.1 Why Machine Learning is used with Example | Machine Learning
8:45
1.2 Difference between Artificial Intelligence(AI) and Machine Learning(ML) | Machine Learning
16:29
1.3 What is Machine Learning and it's Applications | Machine Learning
13:36
1.4 Working of Machine Learning | Data in Machine Learning | Machine Learning
9:18
1.5 Data Processing in Machine Learning | Machine Learning
9:44
1.6 Data Cleaning in Machine Learning | Machine Learning
9:25
1.7 Data types and Measurement Scales in Machine Learning | Machine Learning
14:43
1.8 Patterns in Machine Learning | Machine Learning
6:29
1.9 Features in Machine Learning | Machine Learning
19:07
1.10 Learning Paradigms in Machine Learning(Part 1) | Supervised Learning | Machine Learning
6:49
1.11 Learning Paradigms in Machine Learning(Part 2) | Supervised Learning | Machine Learning
11:37
1.12 Learning Paradigms in Machine Learning(Part 3) | Unsupervised Learning | Machine Learning
8:52
1.13 Learning Paradigms in Machine Learning(Part 4) | Unsupervised Learning | Machine Learning
10:57
1.14 Learning Paradigms in Machine Learning(Part 5) | Reinforcement Learning | Machine Learning
8:56
1.15 Dimensionality Reduction | Machine Learning
8:26
1.16 Principal Component Analysis (PCA) | Dimensionality Reduction | Machine Learning
8:54
1.17 Linear Discriminant Analysis (LDA) | LDA vs PCA | Dimensionality Reduction | Machine Learning
14:52
2.1 What is Regression | Supervised Learning | Machine Learning
16:26
2.2 Terminologies of Regression | Supervised Learning | Machine Learning
13:10
2.3 Linear Regression (Part 1) | Types of Regression | Supervised Learning | Machine Learning
12:52
2.4 Linear Regression (Part 2) | Types of Regression | Supervised Learning | Machine Learning
10:42
2.5 Linear Regression (Part 3) | Types of Regression | Supervised Learning | Machine Learning
9:11
2.6 Model Performance and Applications of Linear Regression (Part 4) | Machine Learning
45:22
2.7 Least-Square Method in Regression | Machine Learning
10:25
2.8 Logistic Regression and Polynomial Regression | Types of Regression | Machine Learning
2.9 Support Vector Regression and Decision Tree Regression | Types of Regression | Machine Learning
22:15
2.10 Linear Regression using Gradient Descent | Machine Learning
8:20
2.11 Polynomial Regression | Types of Regression | Machine Learning
19:05
2.12 Overfitting and Underfitting in Machine Learning | Machine Learning
20:22
2.13 Regularization and It’s Techniques | Types of Regression | Machine Learning
13:49
3.1 Classification in Machine Learning | Machine Learning
15:28
3.2 Classification Types in Machine Learning | Machine Learning
22:33
3.3 Classification Models and Linear Classifier | Machine Learning
6:42
3.5 Logistic Regression in Machine Learning(Part 2) | Machine Learning
16:13
3.4 Logistic Regression in Machine Learning(Part 1) | Machine Learning
10:15
3.6 Support Vector Machine (SVM) | Machine Learning
15:36
3.7 Types of Support Vector Machine (SVM) | Machine Learning
9:38
3.8 Non Linear Support Vector Machine (Part 1)| Soft Margin SVM | Machine Learning
9:26
3.10 Non Linear Support Vector Machine (Part 2) | Kernel Trick SVM | Machine Learning
12:22
3.9 Hard Margin SVM vs Soft Margin SVM | Non Linear Support Vector Machine | Machine Learning
11:47
3.11 Non Linear Support Vector Machine (Part 3) | Hyperparameters in Kernelized | Machine Learning
8:14
3.12 Non Linear Support Vector Machine (Part 4) | Kernel Functions in SVM | Machine Learning
12:12
4.1 Decision Tree Classification (Part 1) | Tree Based Model | Machine Learning
10:28
4.2 Decision Tree Classification (Part 2) | Tree Based Model | Machine Learning
26:02
4.3 Attribute Selection Measures(Part 1)| Entropy and Information Gain | Machine Learning
4.4 Attribute Selection Measures(Part 2)| Gini Index | Machine Learning
13:42
4.5 Ensemble Learning(Part 1) | Bagging : Random Forest | Machine Learning
18:32
4.7 Ensemble Learning(Part 3) | Boosting: Adaptive and Gradient | Machine Learning
12:40
4.6 Ensemble Learning(Part 2) | Bagging : Random Forest | Machine Learning
11:39
4.8 Probabilistic Models(Part 1) | Machine Learning
10:58
4.9 Conditional Probability : Bayes Theorem | Probabilistic Models(Part 2) | Machine Learning
22:16
4.10 Maximum Likelihood Estimation : MLE (Part 1) | Probabilistic Models | Machine Learning
15:26
4.11 Maximum Likelihood Estimation : MLE (Part 2) | Probabilistic Models | Machine Learning
19:55
4.12 Maximum A Posteriori (MAP) | Probabilistic Models | Machine Learning
20:37
4.13 Naïve Bayes Classifier| Probabilistic Models | Machine Learning
13:25
4.14 Bayesian networks for Learning and Inferencing (Part 1) | Machine Learning
28:28
4.15 Bayesian networks for Learning and Inferencing (Part 2) | Machine Learning
14:34
5.1 Distance Based Model (Part 1) | Machine Learning
17:52
5.2 Types of Distance Based Model (Part 2) | Distance Based Model | Machine Learning
8:05
5.3 Types of Distance Based Model (Part 3) | Distance Based Model | Machine Learning
10:09
5.4 K-Nearest Neighbour KNN (Part 1) | Distance Based Model | Machine Learning
9:39
5.5 K-Nearest Neighbour KNN (Part 2) | Distance Based Model | Machine Learning
11:42
5.6 K-Nearest Neighbour Algorithms (Part 3) | Distance Based Model | Machine Learning
15:37
5.7 K-Nearest Neighbour Example (Part 4) | Distance Based Model | Machine Learning
19:43
5.8 Clustering as Learning Task | Distance Based Model | Machine Learning
23:18
5.9 K-Means Clustering | Distance Based Model | Machine Learning
25:31
5.10 Hierarchical Clustering (Part 1) | Distance Based Model | Machine Learning
25:09
5.11 Performance Measures in Hierarchical Clustering (Part 2) | Single Linkage | Machine Learning
5.12 Performance Measures in Hierarchical Clustering (Part 3) | Complete Linkage | Machine Learning
15:40
5.13 Association Rule Mining (Part 1) | Rule Based Models | Machine Learning
23:15
5.14 Association Rule Mining Working and It's Types (Part 2) | Rule Based Models | Machine Learning
14:30
5.15 Aprior Algorithm - Association Rule Mining (Part 3) | Rule Based Models | Machine Learning
20:02
5.16 Aprior Algorithm Working with Example (Part 4) | Rule Based Models | Machine Learning
11:19
6.1 Neural Network Introduction | Machine Learning
14:23
6.2 Artificial Neural Network Introduction | Neural Network | Machine Learning
23:12
6.3 Biological Neural Network Introduction | Neural Network | Machine Learning
25:37
6.4 Analogy between Artificial & Biological Neural | ANN Transformation Function | Machine Learning
17:51
6.5 Important Terminologies of ANN | Machine Learning
14:15
6.6 McCulloch-Pitts Model of Neuron | Artificial Neural Network | Machine Learning
18:54
6.7 Artificial Neural Network Model : Interconnection and Learning Rules (Part 1) | Machine Learning
13:50
6.8 Artificial Neural Network Model : Learning Rules (Part 2) | Machine Learning
7:32
6.9 Artificial Neural Network Model : Activation Function (Part 3) | Machine Learning
10:44
6.10 Artificial Neural Network Model : Activation Function (Part 4) | Machine Learning
11:52
6.11 Artificial Neural Network Model : Types of Activation Function (Part 5) | Machine Learning
14:53
6.12 Perceptron and its Learning Algorithm (Part 1) | Artificial Neural Network | Machine Learning
19:28
6.13 Perceptron and its Learning Algorithm (Part 2) | Artificial Neural Network | Machine Learning
28:48
6.14 Perceptron Learning Rules (Part 3) | Artificial Neural Network | Machine Learning
22:17
6.15 Perceptron with Sigmoid Activation Function | Artificial Neural Network | Machine Learning
23:20
6.16 Error Back Propagation(EBP) Perceptron Learning | Artificial Neural Network | Machine Learning
8:35
6.17 Deep Learning in Neural Network | Artificial Neural Network | Machine Learning
6.18 Applications of Neural Network | Artificial Neural Network | Machine Learning
